Researchers have discovered a novel biomarker that can predict whether neurons will repair following an injury. Scientists may be able to use the data to build regenerative therapeutics for spinal cord injury and other neurological diseases.
Neurons, the primary cells that comprise our brain and spinal cord, are among the slowest to repair following injury, and many neurons fail to regenerate completely. While scientists have made strides in their understanding of neural regeneration, it is still unclear why certain neurons recover while others do not.
Researchers at the University of California San Diego School of Medicine uncovered a new biomarker that can be used to predict whether or not neurons would recover after an injury using single-cell RNA sequencing, a process that detects which genes are activated in individual cells. In mice, they discovered that the biomarker was consistently reliable in neurons across the nervous system and at various developmental stages. The findings were published in the journal Neuron on October 16, 2023.
Single-cell sequencing technology is helping us look at the biology of neurons in much more detail than has ever been possible, and this study really demonstrates that capability. What we’ve discovered here could be just the beginning of a new generation of sophisticated biomarkers based on single-cell data.
Binhai Zheng
“Single-cell sequencing technology is helping us look at the biology of neurons in much more detail than has ever been possible, and this study really demonstrates that capability,” said senior author Binhai Zheng, PhD, professor in the Department of Neurosciences at UC San Diego School of Medicine. “What we’ve discovered here could be just the beginning of a new generation of sophisticated biomarkers based on single-cell data.”
The researchers concentrated on neurons of the corticospinal tract, a vital portion of the central nervous system that aids in movement regulation. These neurons are among the least likely to repair axons, which are the long, thin structures that neurons use to connect with one another after injury. This is why brain and spinal cord injuries are so catastrophic.
“If you get an injury in your arm or leg, those nerves can regenerate and it’s often possible to make a full functional recovery, but this isn’t the case for the central nervous system,” explained first author Hugo Kim, PhD, a postdoctoral fellow in the Zheng group. “It’s extremely difficult to recover from most brain and spinal cord injuries because those cells have very limited regenerative capacity. Once they’re gone, they’re gone.”
The researchers examined gene expression in neurons from mice with spinal cord injury using single-cell RNA sequencing. They used proven molecular approaches to induce these neurons to regenerate, however this only worked for a subset of the cells. The researchers were able to compare sequencing data from regenerating and non-regenerating neurons using this experimental setup.
Furthermore, by focusing on a very small number of cells (just over 300), the researchers were able to examine each individual cell in great detail.
“Just like how every person is different, every cell has its own unique biology,” said Zheng. “Exploring minute differences between cells can tell us a lot about how those cells work.”
Using a computer algorithm to analyze their sequencing data, the researchers identified a unique pattern of gene expression that can predict whether or not an individual neuron will ultimately regenerate after an injury. The pattern also included some genes that had never been previously implicated in neuronal regeneration.
“It’s like a molecular fingerprint for regenerating neurons,” added Zheng.
To validate their findings, the researchers tested this molecular fingerprint, which they named the Regeneration Classifier, on 26 published single-cell RNA sequencing datasets. These datasets included neurons from various parts of the nervous system and at different developmental stages.
The researchers discovered that, with a few exceptions, the Regeneration Classifier accurately predicted the regeneration capability of individual neurons and could reproduce established trends from earlier research, such as a rapid drop in neural regeneration shortly after birth.
“Validating the results against many sets of data from completely different lines of research tells us that we’ve uncovered something fundamental about the underlying biology of neuronal regeneration,” Zheng, the researcher, said. “We need to do more work to refine our approach, but I think we’ve come across a pattern that could be universal to all regenerating neurons.”
While the results in mice are promising, the researchers emphasize that the Regeneration Classifier is now a tool to assist neuroscience researchers in the lab rather than a diagnostic test for patients in the clinic.
“There are still a lot of barriers to using single-cell sequencing in clinical contexts, such as high cost, difficulty analyzing large amounts of data and, most importantly, accessibility to tissues of interest,” Zheng added. “For now, we’re interested in exploring how we can use the Regeneration Classifier in preclinical contexts to predict the effectiveness of new regenerative therapies and help move those treatments closer to clinical trials.”
